DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills

Abstract

Objective: An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills. Approach: We propose DistaNet as a neural network based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users. Results: Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills. Significance: We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.
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